ArticleNature communications2025
Inverse design of promising electrocatalysts for CO
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Data-driven catalyst design for direct catalytic NNature communications · 2026Article
- Probing Electrocatalyst Design for Product Selectivity in COAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- On-chip electrocatalytic microcells for high-throughput experimentation of electrocatalysts.iScience · 2026Review
- Generative intelligence explores the chemical space of ten million catalysts.Chemical science · 2026Article
- Subsurface hydrogen as a hidden driver of copper surface reconstruction in CONational science review · 2026Article
- Science-Towards-Technology Breakthrough in COAdvanced materials (Deerfield Beach, Fla.) · 2026Review
- Continuous discovery of novel 2D materials via dual active learning-driven generative models.National science review · 2026Article
- AI-Accelerated Discovery of Electrocatalyst Materials.ACS materials Au · 2026Review
- A Generative Framework for Predicting Antiferromagnets.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Modeling the potential energy surface by force fields for heterogeneous catalysis: classification, applications, and challenges.Chemical science · 2025Review
- Inverse design of promising electrocatalysts for CONature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties. Applied to the design of alloy electrocatalysts for CO
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.